145,147 research outputs found
NASA MSFC hardware in the loop simulations of automatic rendezvous and capture systems
Two complementary hardware-in-the-loop simulation facilities for automatic rendezvous and capture systems at MSFC are described. One, the Flight Robotics Laboratory, uses an 8 DOF overhead manipulator with a work volume of 160 by 40 by 23 feet to evaluate automatic rendezvous algorithms and range/rate sensing systems. The other, the Space Station/Station Operations Mechanism Test Bed, uses a 6 DOF hydraulic table to perform docking and berthing dynamics simulations
Black-box Integration of Heterogeneous Modeling Languages for Cyber-Physical Systems
Robots belong to a class of Cyber-Physical Systems where complex software as
a mobile device has to full tasks in a complex environment. Modeling robotics
applications for analysis and code generation requires modeling languages for
the logical software architecture and the system behavior. The
MontiArcAutomaton modeling framework integrates six independently developed
modeling languages to model robotics applications: a component & connector
architecture description language, automata, I/O tables, class diagrams, OCL,
and a Java DSL. We describe how we integrated these languages into
MontiArcAutomaton a-posteriori in a black-box integration fashion.Comment: 6 pages, 4 figures. GEMOC Workshop 2013 - International Workshop on
The Globalization of Modeling Languages, Miami, Florida (USA), Volume 1102 of
CEUR Workshop Proceedings, CEUR-WS.org, 201
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
A Playful Experiential Learning System With Educational Robotics
This article reports on two studies that aimed to evaluate the effective impact of
educational robotics in learning concepts related to Physics and Geography. The
reported studies involved two courses from an upper secondary school and two courses
froma lower secondary school. Upper secondary school classes studied topics ofmotion
physics, and lower secondary school classes explored issues related to geography.
In each grade, there was an “experimental group” that carried out their study using
robotics and cooperative learning and a “control group” that studied the same concepts
without robots. Students in both classes were subjected to tests before and after the
robotics laboratory, to check their knowledge in the topics covered. Our initial hypothesis
was that classes involving educational robotics and cooperative learning are more
effective in improving learning and stimulating the interest and motivation of students.
As expected, the results showed that students in the experimental groups had a far
better understanding of concepts and higher participation to the activities than students
in the control groups
A factorization approach to inertial affine structure from motion
We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives
Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo
Asymptotically-optimal motion planners such as RRT* have been shown to
incrementally approximate the shortest path between start and goal states. Once
an initial solution is found, their performance can be dramatically improved by
restricting subsequent samples to regions of the state space that can
potentially improve the current solution. When the motion planning problem lies
in a Euclidean space, this region , called the informed set, can be
sampled directly. However, when planning with differential constraints in
non-Euclidean state spaces, no analytic solutions exists to sampling
directly.
State-of-the-art approaches to sampling in such domains such as
Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional
state space. This may cause the planning algorithm to spend most of its time
trying to produces samples in rather than explore it. In this paper,
we suggest an alternative approach to produce samples in the informed set
for a wide range of settings. Our main insight is to recast this
problem as one of sampling uniformly within the sub-level-set of an implicit
non-convex function. This recasting enables us to apply Monte Carlo sampling
methods, used very effectively in the Machine Learning and Optimization
communities, to solve our problem. We show for a wide range of scenarios that
using our sampler can accelerate the convergence rate to high-quality solutions
in high-dimensional problems
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Bio-inspired Tensegrity Soft Modular Robots
In this paper, we introduce a design principle to develop novel soft modular
robots based on tensegrity structures and inspired by the cytoskeleton of
living cells. We describe a novel strategy to realize tensegrity structures
using planar manufacturing techniques, such as 3D printing. We use this
strategy to develop icosahedron tensegrity structures with programmable
variable stiffness that can deform in a three-dimensional space. We also
describe a tendon-driven contraction mechanism to actively control the
deformation of the tensegrity mod-ules. Finally, we validate the approach in a
modular locomotory worm as a proof of concept.Comment: 12 pages, 7 figures, submitted to Living Machine conference 201
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